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Engineering & Data

Data Engineer resume example & template

A mid-level data engineer resume from insurance and health tech. The bullets report what data engineering teams are judged on: pipeline run time, data freshness, warehouse spend, and how often analysts get bad data.

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  • Every pipeline claim has a before and after: run time, freshness, or monthly spend.
  • Reliability work (dbt tests, monitors, on-call runbooks, data contracts) gets its own bullets.
  • A SnowPro Core certification backs the Snowflake experience instead of replacing it.
100%
Kwame Asante
Data Engineer · dbt, Snowflake, Airflow & Kafka
Pittsburgh, PA(412) 555-0162kwame.asante@email.comlinkedin.com/in/kwameasantegithub.com/kasante

Summary

Data engineer with 8 years building batch and streaming pipelines for insurance and pharmacy data. Moved a legacy SSIS estate to dbt on Snowflake and owns warehouse cost, testing and on-call for the claims domain. Works in SQL and Python, with Terraform for the infrastructure around it.

Experience

Data Engineer II · Highmark HealthJan 2022 – Present
Pittsburgh, PA
  • Moved 140 nightly SSIS jobs for claims reporting to dbt models on Snowflake under Airflow; the nightly run fell from 6.5 hours to under 2.
  • Built a Kafka to Snowflake path with Snowpipe Streaming for member eligibility events, taking dashboard freshness from next-day to under 10 minutes.
  • Cut Snowflake spend by about $18,000 a month by right-sizing warehouses, tightening auto-suspend, and clustering the two largest claims tables.
  • Added dbt tests and Monte Carlo monitors across 320 models; bad-data tickets from actuarial analysts fell from about 12 a month to 3.
  • Wrote a data contract template with 4 source-system owners so schema changes arrive with notice instead of as failed loads.
Data Engineer I · UPMC Health PlanJul 2018 – Dec 2021
Pittsburgh, PA
  • Maintained 60+ Python ETL jobs loading prior-authorization data from PostgreSQL into Redshift for product and finance reporting.
  • Rewrote the daily prescriber-activity job in PySpark on EMR, cutting run time from 3 hours to 25 minutes as volume passed 40M rows a day.
  • Handled pipeline on-call one week in six and wrote runbooks for the 8 most common failure modes.

Education

B.S. Computer Science and Engineering · The Ohio State UniversityMay 2018

Skills

Languages: SQL, Python, PySpark, Bash
Data platform: Snowflake, dbt, Airflow, Kafka, Spark, Redshift, PostgreSQL
Cloud and ops: AWS (S3, EMR, Glue, IAM), Terraform, GitHub Actions, Docker, Monte Carlo
Modeling: Dimensional modeling, Slowly changing dimensions (Type 2), Data contracts

Certifications

SnowPro Core Certification (Snowflake)

How to write a data engineer resume

What hiring teams in this field actually screen for, and the terms that get matched.

What data engineering hiring managers screen for

Data engineering managers are hiring someone to keep data moving and keep analysts trusting it. They scan for ownership of pipelines end to end, recent experience with the stack they run, and evidence of reliability: tests, monitoring, SLAs met, on-call. Give scale in the units the job uses, such as number of models or jobs, rows per day, refresh cadence, and warehouse spend. A migration bullet should say what moved, from what to what, and what changed afterward, whether that is run time, freshness, or the number of broken dashboards analysts report.

Tools and credentials that get matched

Current postings repeat the same core: advanced SQL (window functions, CTEs, query tuning), Python, dbt, Airflow, and a cloud warehouse such as Snowflake, BigQuery, Redshift or Databricks. Streaming roles add Kafka or Kinesis and Spark. Use the posting's terms for patterns too: ELT, dimensional modeling, slowly changing dimensions, data quality, data contracts, observability. Vendor certifications like SnowPro Core (Snowflake) or Databricks Certified Data Engineer Associate help an application clear a filter, but only when the experience section shows you used that platform in production.

Common mistakes on data engineer resumes

The biggest is a tool list with no outcome: a bullet that names Airflow, Spark, Kafka and Snowflake but never says what the pipeline did or how well. Close behind is repeating the same bullet five times (built a pipeline, built another pipeline). Pick different kinds of work instead: a migration, a cost fix, a quality fix, an incident. Avoid vague reliability claims with no baseline, and avoid false precision such as a 37.46% improvement nobody measured that way. Say you owned something only if you were the one paged when it broke.

Data Engineer resume FAQ

Quick answers before you start writing.

What should a data engineer resume include?

A strong data engineer resume opens with a concise professional summary, then work experience written as quantified, results-first bullets, followed by education, a skills section, and any certifications. The example above shows each section in order.

What skills should a data engineer resume highlight?

Lead with the skills the job posting asks for. For a data engineer, recruiters and ATS filters commonly look for SQL, Python, PySpark, Bash, Snowflake, and dbt. Group them so they are easy to scan.

Is this data engineer resume template ATS-friendly?

Yes. It uses a single-column structure, standard section headings, real text (no text trapped in images), and no tables or graphics that confuse applicant-tracking systems, so it parses cleanly while still looking polished.

How long should a data engineer resume be?

One page is ideal for most data engineers. Use two pages only if you have ten or more years of directly relevant experience, then tune the text size and margins so it stays clean.

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